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Repo containing all scripts associated with Mahoenui giant wētā population genomic analysis from GBS data

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natforsdick/Weta_GBS

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Mahoenui giant wētā population genomics

Developed by Natalie Forsdick, 2021. This project is led by Manaaki Whenua - Landcare Research, and funded by the New Zealand Ministry of Business, Innovation and Employment through New Zealand's Biological Heritage National Science Challenge and the Strategic Science Investment Fund for Crown Research Institutes, with support from Genomics Aotearoa.

This repo contains scripts used to analyse paired-end genotyping-by-sequencing (GBS) data from Mahoenui giant wētā, Deinacrida mahoenui.

This work is associated with the publication: Forsdick et al., 2025. 'Population genomic analysis of Mahoenui giant wētā (Deinacrida mahoenui) reveals minimal reduction in genomic diversity following translocation'. Insect Conservation and Diversity. DOI: 10.1111/icad.12810

Scripts were run on the NeSI platform via SLURM workload manager, except for R scripts which were run locally.

The workflow moves through demultiplexing, quality control, and mapping, before processing through Stacks ref_map and populations pipelines after which data are output in formats for analysis via genetics packages such as adegenet and SNPRelate in R, and FastSTRUCTURE.

Software

Pipeline

  1. 01-stacks_process_radtags.sl - Demultiplex raw paired-end GBS with Stacks process_radtags.
  2. 02-trimgalore_PE2.sl - Trim and adapter removal
  3. 03-bowtie-index-ref.sl - Index the Poor Knights giant wētā reference genome assembly
  4. 04-bowtie-align.sl - Align individual data to the reference genome assembly, collect mapping statistics
  5. 05-refmap.sl - Run Stacks ref_map.pl
  6. 06-stacks-popns.sl - Call and filter variants and collect preliminary statistics
  7. 07-export-format.sh - Convert VCF to various formats for downstream processing
  8. SNP-filtering.Rmd - Additional SNP filtering analysis
  9. Population genomic analyses:

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Repo containing all scripts associated with Mahoenui giant wētā population genomic analysis from GBS data

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